Triple

T1404832
Position Surface form Disambiguated ID Type / Status
Subject Lake Balaton E31666 entity
Predicate hasResortTown P847 FINISHED
Object Zamárdi
Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
 E167008 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Zamárdi | Statement: [Lake Balaton, hasResortTown, Zamárdi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zamárdi
Context triple: [Lake Balaton, hasResortTown, Zamárdi]
  • A. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • B. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • C. Gárdony
    Gárdony is a Hungarian town and popular resort area on the southern shore of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
  • D. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • E. Miskolc
    Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Zamárdi
Triple: [Lake Balaton, hasResortTown, Zamárdi]
Generated description
Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zamárdi
Target entity description: Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • A. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • B. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • C. Gárdony
    Gárdony is a Hungarian town and popular resort area on the southern shore of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
  • D. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • E. Miskolc
    Miskolc is a large industrial and cultural city in northeastern Hungary, known for its steel industry, historic center, and nearby cave baths.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c48ff58c8190aeaf09d3e7cad7c7 completed March 1, 2026, 10:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e67dbf88190a2a15baca5b9e79d completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad0edd84e4819081a23828e69cd9a1 completed March 8, 2026, 5:53 a.m.
NED2 Entity disambiguation (via description) batch_69ad0f90cdec81908a981e12184cdd75 completed March 8, 2026, 5:56 a.m.
Created at: March 1, 2026, 7:59 p.m.